Add Lora

4 files changed, 214 insertions, 37 deletions

diff --git a/training/functional.py b/training/functional.py
index c373ac9..8f47734 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -34,7 +34,7 @@ def const(result=None):
 @dataclass
 class TrainingCallbacks():
     on_prepare: Callable[[], None] = const()
-    on_model: Callable[[], torch.nn.Module] = const(None)
+    on_accum_model: Callable[[], torch.nn.Module] = const(None)
     on_log: Callable[[], dict[str, Any]] = const({})
     on_train: Callable[[int], _GeneratorContextManager] = const(nullcontext())
     on_before_optimize: Callable[[float, int], None] = const()
@@ -51,7 +51,11 @@ class TrainingStrategyPrepareCallable(Protocol):
         accelerator: Accelerator,
         text_encoder: CLIPTextModel,
         unet: UNet2DConditionModel,
-        *args
+        optimizer: torch.optim.Optimizer,
+        train_dataloader: DataLoader,
+        val_dataloader: Optional[DataLoader],
+        lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
+        **kwargs
     ) -> Tuple: ...
 
 
@@ -92,7 +96,6 @@ def save_samples(
     sample_scheduler: DPMSolverMultistepScheduler,
     train_dataloader: DataLoader,
     val_dataloader: Optional[DataLoader],
-    dtype: torch.dtype,
     output_dir: Path,
     seed: int,
     step: int,
@@ -107,15 +110,6 @@ def save_samples(
     grid_cols = min(batch_size, 4)
     grid_rows = (num_batches * batch_size) // grid_cols
 
-    unet = accelerator.unwrap_model(unet)
-    text_encoder = accelerator.unwrap_model(text_encoder)
-
-    orig_unet_dtype = unet.dtype
-    orig_text_encoder_dtype = text_encoder.dtype
-
-    unet.to(dtype=dtype)
-    text_encoder.to(dtype=dtype)
-
     pipeline = VlpnStableDiffusion(
         text_encoder=text_encoder,
         vae=vae,
@@ -172,11 +166,6 @@ def save_samples(
         image_grid = make_grid(all_samples, grid_rows, grid_cols)
         image_grid.save(file_path, quality=85)
 
-    unet.to(dtype=orig_unet_dtype)
-    text_encoder.to(dtype=orig_text_encoder_dtype)
-
-    del unet
-    del text_encoder
     del generator
     del pipeline
 
@@ -393,7 +382,7 @@ def train_loop(
     )
     global_progress_bar.set_description("Total progress")
 
-    model = callbacks.on_model()
+    model = callbacks.on_accum_model()
     on_log = callbacks.on_log
     on_train = callbacks.on_train
     on_before_optimize = callbacks.on_before_optimize
@@ -559,8 +548,10 @@ def train(
     prior_loss_weight: float = 1.0,
     **kwargs,
 ):
-    text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler = strategy.prepare(
-        accelerator, text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler)
+    text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, extra = strategy.prepare(
+        accelerator, text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, **kwargs)
+
+    kwargs.update(extra)
 
     vae.to(accelerator.device, dtype=dtype)
 
diff --git a/training/strategy/dreambooth.py b/training/strategy/dreambooth.py
index e88bf90..b4c77f3 100644
--- a/training/strategy/dreambooth.py
+++ b/training/strategy/dreambooth.py
@@ -61,14 +61,11 @@ def dreambooth_strategy_callbacks(
     save_samples_ = partial(
         save_samples,
         accelerator=accelerator,
-        unet=unet,
-        text_encoder=text_encoder,
         tokenizer=tokenizer,
         vae=vae,
         sample_scheduler=sample_scheduler,
         train_dataloader=train_dataloader,
         val_dataloader=val_dataloader,
-        dtype=weight_dtype,
         output_dir=sample_output_dir,
         seed=seed,
         batch_size=sample_batch_size,
@@ -94,7 +91,7 @@ def dreambooth_strategy_callbacks(
         else:
             return nullcontext()
 
-    def on_model():
+    def on_accum_model():
         return unet
 
     def on_prepare():
@@ -172,11 +169,29 @@ def dreambooth_strategy_callbacks(
     @torch.no_grad()
     def on_sample(step):
         with ema_context():
-            save_samples_(step=step)
+            unet_ = accelerator.unwrap_model(unet)
+            text_encoder_ = accelerator.unwrap_model(text_encoder)
+
+            orig_unet_dtype = unet_.dtype
+            orig_text_encoder_dtype = text_encoder_.dtype
+
+            unet_.to(dtype=weight_dtype)
+            text_encoder_.to(dtype=weight_dtype)
+
+            save_samples_(step=step, unet=unet_, text_encoder=text_encoder_)
+
+            unet_.to(dtype=orig_unet_dtype)
+            text_encoder_.to(dtype=orig_text_encoder_dtype)
+
+            del unet_
+            del text_encoder_
+
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
 
     return TrainingCallbacks(
         on_prepare=on_prepare,
-        on_model=on_model,
+        on_accum_model=on_accum_model,
         on_train=on_train,
         on_eval=on_eval,
         on_before_optimize=on_before_optimize,
@@ -191,9 +206,13 @@ def dreambooth_prepare(
     accelerator: Accelerator,
     text_encoder: CLIPTextModel,
     unet: UNet2DConditionModel,
-    *args
+    optimizer: torch.optim.Optimizer,
+    train_dataloader: DataLoader,
+    val_dataloader: Optional[DataLoader],
+    lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
+    **kwargs
 ):
-    return accelerator.prepare(text_encoder, unet, *args)
+    return accelerator.prepare(text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler) + ({})
 
 
 dreambooth_strategy = TrainingStrategy(
diff --git a/training/strategy/lora.py b/training/strategy/lora.py
new file mode 100644
index 0000000..88d1824
--- /dev/null
+++ b/training/strategy/lora.py
@@ -0,0 +1,147 @@
+from contextlib import nullcontext
+from typing import Optional
+from functools import partial
+from contextlib import contextmanager, nullcontext
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+
+from accelerate import Accelerator
+from transformers import CLIPTextModel
+from diffusers import AutoencoderKL, UNet2DConditionModel, DPMSolverMultistepScheduler
+from diffusers.loaders import AttnProcsLayers
+
+from slugify import slugify
+
+from models.clip.tokenizer import MultiCLIPTokenizer
+from training.util import EMAModel
+from training.functional import TrainingStrategy, TrainingCallbacks, save_samples
+
+
+def lora_strategy_callbacks(
+    accelerator: Accelerator,
+    unet: UNet2DConditionModel,
+    text_encoder: CLIPTextModel,
+    tokenizer: MultiCLIPTokenizer,
+    vae: AutoencoderKL,
+    sample_scheduler: DPMSolverMultistepScheduler,
+    train_dataloader: DataLoader,
+    val_dataloader: Optional[DataLoader],
+    sample_output_dir: Path,
+    checkpoint_output_dir: Path,
+    seed: int,
+    lora_layers: AttnProcsLayers,
+    max_grad_norm: float = 1.0,
+    sample_batch_size: int = 1,
+    sample_num_batches: int = 1,
+    sample_num_steps: int = 20,
+    sample_guidance_scale: float = 7.5,
+    sample_image_size: Optional[int] = None,
+):
+    sample_output_dir.mkdir(parents=True, exist_ok=True)
+    checkpoint_output_dir.mkdir(parents=True, exist_ok=True)
+
+    weight_dtype = torch.float32
+    if accelerator.state.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.state.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+
+    save_samples_ = partial(
+        save_samples,
+        accelerator=accelerator,
+        unet=unet,
+        text_encoder=text_encoder,
+        tokenizer=tokenizer,
+        vae=vae,
+        sample_scheduler=sample_scheduler,
+        train_dataloader=train_dataloader,
+        val_dataloader=val_dataloader,
+        output_dir=sample_output_dir,
+        seed=seed,
+        batch_size=sample_batch_size,
+        num_batches=sample_num_batches,
+        num_steps=sample_num_steps,
+        guidance_scale=sample_guidance_scale,
+        image_size=sample_image_size,
+    )
+
+    def on_prepare():
+        lora_layers.requires_grad_(True)
+
+    def on_accum_model():
+        return unet
+
+    @contextmanager
+    def on_train(epoch: int):
+        tokenizer.train()
+        yield
+
+    @contextmanager
+    def on_eval():
+        tokenizer.eval()
+        yield
+
+    def on_before_optimize(lr: float, epoch: int):
+        if accelerator.sync_gradients:
+            accelerator.clip_grad_norm_(lora_layers.parameters(), max_grad_norm)
+
+    @torch.no_grad()
+    def on_checkpoint(step, postfix):
+        print(f"Saving checkpoint for step {step}...")
+        orig_unet_dtype = unet.dtype
+        unet.to(dtype=torch.float32)
+        unet.save_attn_procs(checkpoint_output_dir.joinpath(f"{step}_{postfix}"))
+        unet.to(dtype=orig_unet_dtype)
+
+    @torch.no_grad()
+    def on_sample(step):
+        orig_unet_dtype = unet.dtype
+        unet.to(dtype=weight_dtype)
+        save_samples_(step=step)
+        unet.to(dtype=orig_unet_dtype)
+
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+    return TrainingCallbacks(
+        on_prepare=on_prepare,
+        on_accum_model=on_accum_model,
+        on_train=on_train,
+        on_eval=on_eval,
+        on_before_optimize=on_before_optimize,
+        on_checkpoint=on_checkpoint,
+        on_sample=on_sample,
+    )
+
+
+def lora_prepare(
+    accelerator: Accelerator,
+    text_encoder: CLIPTextModel,
+    unet: UNet2DConditionModel,
+    optimizer: torch.optim.Optimizer,
+    train_dataloader: DataLoader,
+    val_dataloader: Optional[DataLoader],
+    lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
+    lora_layers: AttnProcsLayers,
+    **kwargs
+):
+    weight_dtype = torch.float32
+    if accelerator.state.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.state.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+
+    lora_layers, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare(
+        lora_layers, optimizer, train_dataloader, val_dataloader, lr_scheduler)
+    unet.to(accelerator.device, dtype=weight_dtype)
+    text_encoder.to(accelerator.device, dtype=weight_dtype)
+    return text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, {"lora_layers": lora_layers}
+
+
+lora_strategy = TrainingStrategy(
+    callbacks=lora_strategy_callbacks,
+    prepare=lora_prepare,
+)
diff --git a/training/strategy/ti.py b/training/strategy/ti.py
index 14bdafd..d306f18 100644
--- a/training/strategy/ti.py
+++ b/training/strategy/ti.py
@@ -59,14 +59,11 @@ def textual_inversion_strategy_callbacks(
     save_samples_ = partial(
         save_samples,
         accelerator=accelerator,
-        unet=unet,
-        text_encoder=text_encoder,
         tokenizer=tokenizer,
         vae=vae,
         sample_scheduler=sample_scheduler,
         train_dataloader=train_dataloader,
         val_dataloader=val_dataloader,
-        dtype=weight_dtype,
         output_dir=sample_output_dir,
         seed=seed,
         batch_size=sample_batch_size,
@@ -94,7 +91,7 @@ def textual_inversion_strategy_callbacks(
         else:
             return nullcontext()
 
-    def on_model():
+    def on_accum_model():
         return text_encoder.text_model.embeddings.temp_token_embedding
 
     def on_prepare():
@@ -149,11 +146,29 @@ def textual_inversion_strategy_callbacks(
     @torch.no_grad()
     def on_sample(step):
         with ema_context():
-            save_samples_(step=step)
+            unet_ = accelerator.unwrap_model(unet)
+            text_encoder_ = accelerator.unwrap_model(text_encoder)
+
+            orig_unet_dtype = unet_.dtype
+            orig_text_encoder_dtype = text_encoder_.dtype
+
+            unet_.to(dtype=weight_dtype)
+            text_encoder_.to(dtype=weight_dtype)
+
+            save_samples_(step=step, unet=unet_, text_encoder=text_encoder_)
+
+            unet_.to(dtype=orig_unet_dtype)
+            text_encoder_.to(dtype=orig_text_encoder_dtype)
+
+            del unet_
+            del text_encoder_
+
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
 
     return TrainingCallbacks(
         on_prepare=on_prepare,
-        on_model=on_model,
+        on_accum_model=on_accum_model,
         on_train=on_train,
         on_eval=on_eval,
         on_before_optimize=on_before_optimize,
@@ -168,7 +183,11 @@ def textual_inversion_prepare(
     accelerator: Accelerator,
     text_encoder: CLIPTextModel,
     unet: UNet2DConditionModel,
-    *args
+    optimizer: torch.optim.Optimizer,
+    train_dataloader: DataLoader,
+    val_dataloader: Optional[DataLoader],
+    lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
+    **kwargs
 ):
     weight_dtype = torch.float32
     if accelerator.state.mixed_precision == "fp16":
@@ -176,9 +195,10 @@ def textual_inversion_prepare(
     elif accelerator.state.mixed_precision == "bf16":
         weight_dtype = torch.bfloat16
 
-    prepped = accelerator.prepare(text_encoder, *args)
+    text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare(
+        text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler)
     unet.to(accelerator.device, dtype=weight_dtype)
-    return (prepped[0], unet) + prepped[1:]
+    return text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, {}
 
 
 textual_inversion_strategy = TrainingStrategy(